# Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation

## TL;DR

This paper introduces a nonlinear double-capacitor model for rechargeable batteries that captures nonlinear phenomena, improves accuracy, and is suitable for real-time management, outperforming existing models.

## Contribution

It develops a nonlinear extension of the double-capacitor battery model with novel parameter estimation methods, including a Wiener system approach for better accuracy.

## Key findings

- The model achieves high accuracy and predictive capability.
- It outperforms Rint and Thevenin models in experiments.
- The model is computationally efficient for real-time applications.

## Abstract

This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a battery's nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.